fitVsDatCorrelation=0.659750970786482 cont.fitVsDatCorrelation=0.243624427069032 fstatistic=12362.4324257625,63,945 cont.fstatistic=7415.93823277623,63,945 residuals=-0.494920115490502,-0.0792757248127383,-0.0049645806789089,0.070560271188722,0.834745300513367 cont.residuals=-0.436398474570336,-0.107800984956301,-0.0285284844621514,0.0766878864744191,0.93741425601622 predictedValues: Include Exclude Both Lung 50.1875431908179 52.9986614925069 48.9392930231513 cerebhem 56.2544139733458 60.4534888011463 51.9937948072129 cortex 49.9530958033791 47.6963725660793 46.568388356859 heart 50.3174658796947 47.3445941658404 49.6704344769898 kidney 49.6078040422503 49.8854037316534 47.7061526105476 liver 51.704072237496 46.9020688508604 47.533631917315 stomach 50.9021628010613 49.3027272569757 50.3871073925881 testicle 51.6671544151834 52.6021024623466 52.0025641313826 diffExp=-2.81111830168904,-4.19907482780052,2.25672323729986,2.97287171385427,-0.277599689403054,4.80200338663555,1.59943554408563,-0.934948047163225 diffExpScore=4.50373300429087 diffExp1.5=0,0,0,0,0,0,0,0 diffExp1.5Score=0 diffExp1.4=0,0,0,0,0,0,0,0 diffExp1.4Score=0 diffExp1.3=0,0,0,0,0,0,0,0 diffExp1.3Score=0 diffExp1.2=0,0,0,0,0,0,0,0 diffExp1.2Score=0 cont.predictedValues: Include Exclude Both Lung 49.6463291499113 51.5016683647251 47.9159311478345 cerebhem 50.0417643026441 48.8546576676334 47.5088465627486 cortex 48.7797952451341 47.4767986805526 47.8570360793923 heart 50.02739465427 48.0126972541945 50.2927598783889 kidney 49.3034470858515 50.6019469910961 53.1809502426406 liver 48.9067112189295 50.8451861345914 47.6137852659781 stomach 49.1700601240154 49.2718681867414 50.8282802489758 testicle 49.5452532558814 49.639582329994 48.2536718485702 cont.diffExp=-1.85533921481382,1.18710663501073,1.30299656458151,2.01469740007543,-1.29849990524456,-1.93847491566181,-0.101808062725951,-0.0943290741125722 cont.diffExpScore=5.49056632564125 cont.diffExp1.5=0,0,0,0,0,0,0,0 cont.diffExp1.5Score=0 cont.diffExp1.4=0,0,0,0,0,0,0,0 cont.diffExp1.4Score=0 cont.diffExp1.3=0,0,0,0,0,0,0,0 cont.diffExp1.3Score=0 cont.diffExp1.2=0,0,0,0,0,0,0,0 cont.diffExp1.2Score=0 tran.correlation=0.807834386563195 cont.tran.correlation=-0.064870522716618 tran.covariance=0.00265889811562156 cont.tran.covariance=-1.61417077240631e-05 tran.mean=51.1111957294148 cont.tran.mean=49.4765725403854 weightedLogRatios: wLogRatio Lung -0.214893648532393 cerebhem -0.292701765811681 cortex 0.179737478479896 heart 0.236771725215162 kidney -0.0218018435374714 liver 0.379839558218613 stomach 0.124956582808472 testicle -0.070906548245858 cont.weightedLogRatios: wLogRatio Lung -0.143943768320953 cerebhem 0.0936525669701722 cortex 0.104882760381779 heart 0.159982364105684 kidney -0.101670361587329 liver -0.151959638828707 stomach -0.00805910342125326 testicle -0.00742543930162033 varWeightedLogRatios=0.0532478661412353 cont.varWeightedLogRatios=0.0141803153771330 coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 4.20222087213808 0.0658783227957952 63.7876116725651 0 *** df.mm.trans1 -0.0759223584621109 0.0575740181719915 -1.31869132766289 0.187591810431484 df.mm.trans2 -0.282739966510798 0.0506257307581379 -5.58490637619782 3.05654646709708e-08 *** df.mm.exp2 0.185181518492045 0.0655440071870294 2.82530053378688 0.00482311534815391 ** df.mm.exp3 -0.0604350186581756 0.0655440071870293 -0.922052545333774 0.356736507874789 df.mm.exp4 -0.125057878327888 0.0655440071870294 -1.90799866677416 0.0566942179551883 . df.mm.exp5 -0.0466366631744791 0.0655440071870294 -0.711532070985554 0.476930183652358 df.mm.exp6 -0.0632920678777647 0.0655440071870294 -0.965642330917626 0.334470233923354 df.mm.exp7 -0.0873034222325249 0.0655440071870294 -1.33198176277818 0.183187329043802 df.mm.exp8 -0.0391675679203633 0.0655440071870294 -0.597576645086696 0.55026561498071 df.mm.trans1:exp2 -0.0710638621446757 0.0618656648830989 -1.14868016498259 0.250978553516201 df.mm.trans2:exp2 -0.0535738876394418 0.0459233463520958 -1.16659372400018 0.243668789087001 df.mm.trans1:exp3 0.0557526475820482 0.0618656648830989 0.901188853096433 0.367717529093987 df.mm.trans2:exp3 -0.0449762915468075 0.0459233463520958 -0.97937748704053 0.327644151455791 df.mm.trans1:exp4 0.127643277054746 0.0618656648830989 2.06323293051065 0.0393631741407392 * df.mm.trans2:exp4 0.0122438655034221 0.0459233463520958 0.266615272535846 0.789823560206738 df.mm.trans1:exp5 0.0350179717945299 0.0618656648830989 0.566032416538312 0.571506127286996 df.mm.trans2:exp5 -0.0139015456201166 0.0459233463520959 -0.302711947721164 0.762176010337347 df.mm.trans1:exp6 0.0930617606771653 0.0618656648830989 1.50425540326794 0.132849743297861 df.mm.trans2:exp6 -0.0589128040591129 0.0459233463520959 -1.28285085340747 0.199858879582051 df.mm.trans1:exp7 0.101441983770506 0.0618656648830989 1.63971378893592 0.101397506512183 df.mm.trans2:exp7 0.0150161629903546 0.0459233463520959 0.326983205344514 0.74375299967987 df.mm.trans1:exp8 0.0682229841250548 0.0618656648830989 1.10276005687434 0.270412131686156 df.mm.trans2:exp8 0.0316569992574108 0.0459233463520959 0.689344348181762 0.490775819406161 df.mm.trans1:probe2 -0.298060874694737 0.0405005846065103 -7.3594215389875 4.01023979726478e-13 *** df.mm.trans1:probe3 -0.355045393798702 0.0405005846065103 -8.76642639231508 8.44438323716728e-18 *** df.mm.trans1:probe4 -0.322511885621731 0.0405005846065103 -7.96314148931787 4.79631680810135e-15 *** df.mm.trans1:probe5 -0.285739970533921 0.0405005846065103 -7.05520607443257 3.33037303389268e-12 *** df.mm.trans1:probe6 -0.175488156170881 0.0405005846065103 -4.33297834774147 1.62851211714386e-05 *** df.mm.trans1:probe7 -0.244913742552024 0.0405005846065103 -6.04716561332439 2.12071318382271e-09 *** df.mm.trans1:probe8 -0.30902481303481 0.0405005846065103 -7.63013215826853 5.71716402695573e-14 *** df.mm.trans1:probe9 -0.242088163499468 0.0405005846065103 -5.97739923636937 3.21029638871561e-09 *** df.mm.trans1:probe10 -0.157381379783490 0.0405005846065103 -3.88590390268568 0.000109062777428923 *** df.mm.trans1:probe11 -0.332733100971249 0.0405005846065103 -8.21551353403831 6.90923288600998e-16 *** df.mm.trans1:probe12 -0.351515563729695 0.0405005846065103 -8.67927135237428 1.7220330691664e-17 *** df.mm.trans1:probe13 -0.184852160952618 0.0405005846065103 -4.56418500494692 5.67555503475541e-06 *** df.mm.trans1:probe14 -0.283538879377290 0.0405005846065103 -7.0008589291255 4.82171265402235e-12 *** df.mm.trans1:probe15 -0.26596123665291 0.0405005846065103 -6.56684932419859 8.46941410616164e-11 *** df.mm.trans1:probe16 -0.235977249383980 0.0405005846065103 -5.82651464606383 7.75689327977384e-09 *** df.mm.trans1:probe17 -0.333779775971689 0.0405005846065103 -8.24135698816641 5.6495757593145e-16 *** df.mm.trans1:probe18 -0.362524588741867 0.0405005846065103 -8.951095206749 1.82990962460087e-18 *** df.mm.trans1:probe19 -0.264833353597048 0.0405005846065103 -6.53900076184277 1.01239728314316e-10 *** df.mm.trans1:probe20 -0.303540904405209 0.0405005846065103 -7.49472896142877 1.52612868842293e-13 *** df.mm.trans1:probe21 -0.356582915313439 0.0405005846065103 -8.8043893385213 6.17973551466721e-18 *** df.mm.trans1:probe22 -0.175935931147225 0.0405005846065103 -4.34403436040633 1.55015684032499e-05 *** df.mm.trans1:probe23 -0.292734893190386 0.0405005846065103 -7.2279177210526 1.01079798992733e-12 *** df.mm.trans1:probe24 -0.343453907682859 0.0405005846065103 -8.4802209898879 8.57464555692522e-17 *** df.mm.trans1:probe25 -0.274778409521988 0.0405005846065103 -6.78455415376446 2.05190719155787e-11 *** df.mm.trans1:probe26 -0.227428696701188 0.0405005846065103 -5.61544231795188 2.57743950064668e-08 *** df.mm.trans1:probe27 -0.330284482702887 0.0405005846065103 -8.15505469641531 1.10414927999167e-15 *** df.mm.trans1:probe28 -0.268429379954164 0.0405005846065103 -6.62779025444031 5.71828121413923e-11 *** df.mm.trans2:probe2 0.225462402355772 0.0405005846065103 5.56689254109013 3.37865312041204e-08 *** df.mm.trans2:probe3 0.106886237855987 0.0405005846065103 2.63912827171401 0.00844878463136508 ** df.mm.trans2:probe4 0.0738630819928335 0.0405005846065103 1.82375347690562 0.0685050822451158 . df.mm.trans2:probe5 0.246696225589838 0.0405005846065103 6.09117690489295 1.62910419751214e-09 *** df.mm.trans2:probe6 0.0580925906715909 0.0405005846065103 1.43436425019536 0.151799167178100 df.mm.trans3:probe2 -0.0211425872394605 0.0405005846065103 -0.522031655712493 0.601770627732646 df.mm.trans3:probe3 0.468436335085539 0.0405005846065103 11.5661623069569 4.9623209116638e-29 *** df.mm.trans3:probe4 0.0173646410930837 0.0405005846065103 0.428750381304184 0.66820260578117 df.mm.trans3:probe5 0.0529233183644083 0.0405005846065103 1.30672973930112 0.191622422276571 df.mm.trans3:probe6 -0.098729316412331 0.0405005846065103 -2.43772571116074 0.0149634313332443 * df.mm.trans3:probe7 0.0412626437123139 0.0405005846065103 1.01881600261348 0.308551082314798 df.mm.trans3:probe8 -0.0839385149448051 0.0405005846065103 -2.07252600821254 0.0384870712222563 * cont.coeff: Name Estimate Std-Error t-value Pr(>|t|) Signif df.mm.(Intercept) 3.96109669400987 0.0850229149147882 46.588577890793 4.92435575790444e-247 *** df.mm.trans1 -0.0317326054102688 0.0743053350570749 -0.427056891485578 0.66943516986202 df.mm.trans2 -0.0353532787574987 0.0653378382459801 -0.54108430438734 0.588576998539709 df.mm.exp2 -0.0362988888978875 0.0845914454062688 -0.429108271215301 0.667942239098268 df.mm.exp3 -0.0977514283055478 0.0845914454062688 -1.15557108447640 0.248148724153588 df.mm.exp4 -0.110915492272613 0.0845914454062688 -1.31119041340312 0.190111944042167 df.mm.exp5 -0.128806816086110 0.0845914454062688 -1.52269316911997 0.128170031387012 df.mm.exp6 -0.0215128597302130 0.0845914454062688 -0.25431483794718 0.799307663992729 df.mm.exp7 -0.112905314991339 0.0845914454062688 -1.33471315508426 0.182291724717773 df.mm.exp8 -0.0458875347840155 0.0845914454062688 -0.542460701122089 0.587629071884399 df.mm.trans1:exp2 0.0442323785073502 0.0798441572018625 0.553983911377781 0.579720916438275 df.mm.trans2:exp2 -0.0164655934170362 0.0592689158404896 -0.277811618173514 0.781217738922162 df.mm.trans1:exp3 0.080143170353476 0.0798441572018625 1.00374495970766 0.315758502809963 df.mm.trans2:exp3 0.0163783686091557 0.0592689158404896 0.276339939357669 0.782347406332344 df.mm.trans1:exp4 0.118561787533233 0.0798441572018625 1.48491501054340 0.137899823513855 df.mm.trans2:exp4 0.040766791752163 0.0592689158404896 0.687827526015131 0.491730188169841 df.mm.trans1:exp5 0.121876362081622 0.0798441572018626 1.52642806127307 0.127237898744369 df.mm.trans2:exp5 0.111182667147645 0.0592689158404896 1.87590182089497 0.0609768382931809 . df.mm.trans1:exp6 0.00650303732055946 0.0798441572018626 0.0814466273858766 0.935104015204941 df.mm.trans2:exp6 0.00868410721399024 0.0592689158404896 0.146520433027015 0.88354181084436 df.mm.trans1:exp7 0.10326576597856 0.0798441572018626 1.29334154930689 0.196209014145076 df.mm.trans2:exp7 0.0686444055796546 0.0592689158404896 1.15818561224230 0.247080914257194 df.mm.trans1:exp8 0.0438495407009977 0.0798441572018626 0.549189098334859 0.583005427640495 df.mm.trans2:exp8 0.00906187852771657 0.0592689158404896 0.152894285296272 0.878514281862153 df.mm.trans1:probe2 -0.0341007256036356 0.0522702705967841 -0.652392367866059 0.514306742225998 df.mm.trans1:probe3 -0.0692849477136279 0.0522702705967841 -1.32551346917823 0.185321254034158 df.mm.trans1:probe4 0.0178980585157528 0.0522702705967841 0.342413733684668 0.732115696747791 df.mm.trans1:probe5 -0.0737548559326816 0.0522702705967841 -1.41102877583380 0.158565246970453 df.mm.trans1:probe6 -0.065634143722274 0.0522702705967841 -1.25566871900434 0.20954648100331 df.mm.trans1:probe7 0.0506604803491778 0.0522702705967841 0.969202565258858 0.332692118654868 df.mm.trans1:probe8 -0.0352502021808616 0.0522702705967841 -0.674383388078928 0.500232536823703 df.mm.trans1:probe9 -0.0261840895547253 0.0522702705967841 -0.500936560223896 0.61653242660649 df.mm.trans1:probe10 -0.0721431508753106 0.0522702705967841 -1.38019470822769 0.167853110395484 df.mm.trans1:probe11 -0.0629248149814057 0.0522702705967841 -1.20383564620913 0.2289545661404 df.mm.trans1:probe12 -0.00700243270270969 0.0522702705967841 -0.133965878170535 0.893458080560122 df.mm.trans1:probe13 -0.0232102121924201 0.0522702705967841 -0.444042319418337 0.657113646761909 df.mm.trans1:probe14 -0.0162202682828681 0.0522702705967841 -0.310315368519750 0.75638956899588 df.mm.trans1:probe15 -0.0779926862379892 0.0522702705967841 -1.49210412242992 0.136005598577426 df.mm.trans1:probe16 -0.0552084955077585 0.0522702705967841 -1.05621216185468 0.291141234154557 df.mm.trans1:probe17 -0.0360301010451994 0.0522702705967841 -0.689303893663336 0.490801260021598 df.mm.trans1:probe18 -0.0620656260574713 0.0522702705967841 -1.18739821601937 0.235368959161931 df.mm.trans1:probe19 -0.0129887078841213 0.0522702705967841 -0.248491307502825 0.80380828469051 df.mm.trans1:probe20 0.0158964478658162 0.0522702705967841 0.304120251996442 0.761103230529515 df.mm.trans1:probe21 -0.0823477200139114 0.0522702705967841 -1.57542172775700 0.115493827145040 df.mm.trans1:probe22 -0.0966834524865235 0.0522702705967841 -1.84968341243812 0.0646712570681246 . df.mm.trans1:probe23 -0.0113596124661251 0.0522702705967841 -0.217324539100893 0.828002346596764 df.mm.trans1:probe24 0.0285886522502103 0.0522702705967841 0.546939052042505 0.58454973050135 df.mm.trans1:probe25 -0.0793382624297235 0.0522702705967841 -1.51784678984625 0.129387478752810 df.mm.trans1:probe26 0.0133261150179312 0.0522702705967841 0.254946356041078 0.798820003665429 df.mm.trans1:probe27 -0.0231283315438226 0.0522702705967841 -0.442475833389803 0.658246167497887 df.mm.trans1:probe28 0.0166562115457765 0.0522702705967841 0.318655544645321 0.750058142733735 df.mm.trans2:probe2 0.0243011328950677 0.0522702705967841 0.464913087642649 0.642100800599467 df.mm.trans2:probe3 0.00146963255614852 0.0522702705967841 0.0281160311467556 0.9775755444279 df.mm.trans2:probe4 0.0355343424524 0.0522702705967841 0.679819370489087 0.496785332280203 df.mm.trans2:probe5 0.0918326723622153 0.0522702705967841 1.75688151818876 0.0792618362268341 . df.mm.trans2:probe6 0.0690532422723167 0.0522702705967841 1.32108063501330 0.186794268376147 df.mm.trans3:probe2 -0.00311527636111886 0.0522702705967841 -0.0595993922654482 0.95248730329638 df.mm.trans3:probe3 -0.0439593672092967 0.0522702705967841 -0.841001332256376 0.400559935918561 df.mm.trans3:probe4 -0.0675231914797248 0.0522702705967841 -1.29180872241131 0.196739234274930 df.mm.trans3:probe5 -0.0251862873772209 0.0522702705967841 -0.481847273596675 0.630026013401955 df.mm.trans3:probe6 -0.0564423529284827 0.0522702705967841 -1.07981750015955 0.280498970285801 df.mm.trans3:probe7 0.0471323902743125 0.0522702705967841 0.901705495995888 0.367443087810573 df.mm.trans3:probe8 -0.0474080661888781 0.0522702705967841 -0.906979543966524 0.36464882396671